Multiscale Feature Fusion Back-projection Network for Image Super-resolution

被引：0
|
作者：
Sun C.-W. [1 ]
Chen X. [1 ,2 ]
机构：
[1] School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing
[2] Jiangsu Collaborative Innovation Center for Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 07期
关键词：
Back-projection; Feature fusion; Image super-resolution; Multi-scale convolution;
D O I：
10.16383/j.aas.c200714
中图分类号：
学科分类号：
摘要：
Aiming at the problems that existing image super-resolution reconstruction methods have weak ability to restore image high-frequency details and insufficient feature utilization, a multi-scale feature fusion back projection network is proposed for image super-resolution reconstruction. The network first uses multi-scale convolution kernels in the shallow feature extraction layer to extract feature information of different dimensions to enhance cross-channel information fusion; then builds a multi-scale back projection module to perform feature mapping through recursive learning to improve the early reconstruction capabilities of the network; Finally, local residual feedback is combined with global residual learning to promote the spread and utilization of features, thereby fusing feature information of different depths for image reconstruction. The experimental results of ×2 ~ ×8 SR on the images show that the quality of SR image of this method is better than the existing image super-resolution method in subjective perception and objective evaluation index, and the reconstruction performance is relatively better when the scale factors is large. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
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页码：1689 / 1700
页数：11
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